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Predicting Severe Postoperative Complications after CRS-HIPEC: An Externally Validated Machine-Learning Tool.

World journal of surgery
INTRODUCTION: Current decision support tools designed to predict postoperative complications, following cytoreductive surgery with hyperthermic intraperitoneal chemotherapy (CRS-HIPEC), are limited by small sample sizes and lack of external validatio...

Leveraging GPT-4 enables patient comprehension of radiology reports.

European journal of radiology
OBJECTIVE: To assess the feasibility of using GPT-4 to simplify radiology reports into B1-level Dutch for enhanced patient comprehension.

Spatio-temporal CNN-BiLSTM dynamic approach to emotion recognition based on EEG signal.

Computers in biology and medicine
In this paper, a hybrid CNN-BiLSTM model for EEG-based emotion detection system is presented. The proposed technique is developed by extracting features using Power Spectral Density (PSD) signal. The proposed approach is carried out by combining CNN ...

Machine Learning for Predicting Waitlist Mortality in Pediatric Heart Transplantation.

Pediatric transplantation
BACKGROUND: Waitlist mortality remains a critical issue for pediatric heart transplant (HTx) candidates, particularly for candidates with congenital heart disease. Listing center organ offer acceptance practices have been identified as a factor influ...

Concise multi-class anxiety disorder risk assessment: A novel advanced machine learning approach.

Journal of anxiety disorders
Rapidly assessing anxiety disorder risk is crucial for effective mental health screen and intervention. However, traditional survey tools such as DASS-42 are time-consuming in responding and scoring. We used a novel advanced machine learning approach...

PRECISE framework: Enhanced radiology reporting with GPT for improved readability, reliability, and patient-centered care.

European journal of radiology
BACKGROUND: The PRECISE framework, defined as Patient-Focused Radiology Reports with Enhanced Clarity and Informative Summaries for Effective Communication, leverages GPT-4 to create patient-friendly summaries of radiology reports at a sixth-grade re...

Development of a deep neural network model for ultra-early neurological deterioration in ischemic stroke and analysis of associated risk factors.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
BACKGROUND: In this study, we established a deep neural network (DNN)-based predictive model, aiming to provide a basis for improving the treatment prognosis of early neurological deterioration (END) in patients with ultra-early ischemic stroke after...

Exploring customers' reuse intention to robots under different service failures: A mind perception perspective.

Acta psychologica
Artificial intelligence-based service robot failures (hereafter referred to as robot service failures) are inevitable in service practice, making the mitigation of their adverse effects a critical concern for service managers. The present paper inves...

Deep learning for multiple sclerosis lesion classification and stratification using MRI.

Computers in biology and medicine
BACKGROUND AND OBJECTIVE: Multiple sclerosis (MS) is a chronic neurological disease characterized by inflammation, demyelination, and neurodegeneration within the central nervous system. Conventional magnetic resonance imaging (MRI) techniques often ...

Question-based computational language approach outperform ratings scale in discriminating between anxiety and depression.

Journal of anxiety disorders
Major Depression (MD) and General Anxiety Disorder (GAD) are the most common mental health disorders, which typically are assessed quantitatively by rating scales such as PHQ-9 and GAD-7. However, recent advances in natural language processing (NLP) ...